多强调你的方法的特点，与众不同之处，让审稿人一定能看到。
| 第一句：预热一下 |
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| We now present our xx framework for xx. |
| In this section, we provide a more detailed description of the presented approach, xxx. |
| Given xx, we aim to achieve xxx (你文章是干什么的) |
| Our goal is to ... |
| This section introduces the overall working mechanism and specific technical implementations of the proposed xx. |
| 如果你的方法由多个部分构成的话 |
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| Our xx process is split into two parts: a xx branch, which xxx, and a xx branch that xxx. |
| 章节结构安排 |
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| In the following, we first introduce xx in Sec. 3.1, before proceeding to xx in Sec 3.2. |
| The organization of this section is as follows: We introduce xx in Sec. 3.1, including xx. In Sec. 3.2, we first describe xx. Then we present xx. |
| To begin with, we review the basic idea and pipeline of xxx. |
| To facilitate understanding, we begin with an overview of our framework xx |
插补一些有用的连词：Thereafter
| 最后一句：介绍核心的流程图 |
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| Fig. x gives an overview of the training pipeline. |
| Our model is illustrated in Fig. x. |
| In essence, our method extends, ..., as demonstrated in Fig. 1 |
| Fig.1 depicts xx |
| Fig.1 shows the framework of the proposed method. |
| schematic illustrating |
比较具体，这里就不再过多介绍。
| 训练细节 |
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| We inherit the training procedure from xx with minimal changes. |
| The optimization is performed by Adam with learning rate of 0.002 and betas of 0 and 0.99 |
| Further details can be found in the source code. |
| 有的时候会为了简单起见，省略一些符号标记 |
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| for the sake of simplicity, we |
| For ease of notation, we will use latent codes c to denote the concatenation of all latent variables ci. |
| 对公式的描述 |
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| where λ is a positive constant trading off the importance of the first and second terms of the loss |
| 一些不错的说辞 |
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| we want to avoid investing effort into a component that is not a part of the actual solution. |
just show many examples
$$
f_{j}^{\prime}=\left[\nabla_{\mathbf{x}} f_{j}\left(\mathbf{x}_{0}\right)\right]^{t} \mathbf{d}
$$
where $\nabla_{\mathbf{x}}$ is the symbol for the gradient w.r.t. $\mathbf{x}$ and the superscript $^{t}$ stands for transposition. One seeks for a vector $\mathbf{d}$ such that the scalar product of any objective gradient $\nabla_{x} f_{j}\left(\mathbf{x}{0}\right)$ with the vector d remains strictly positive $f{j}^{\prime} > 0$
we use xx to denote xx